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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Alhenawi, Esraa
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Computer Science & IT Electrical & Electronics Engineering

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Optimizing intrusion detection in 5G networks using dimensionality reduction techniques Salah, Zaher; Elsoud, Esraa; Al-Sit, Waleed; Alhenawi, Esraa; Alshraiedeh, Fuad; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5652-5671

Abstract

The proliferation of internet of things (IoT) technologies has expanded the user base of the internet, but it has also exposed users to increased cyber threats. Intrusion detection systems (IDSs) play a vital role in safeguarding against cybercrimes by enabling early threat response. This research uniquely centers on the critical dimensionality aspects of wireless datasets. This study focuses on the intricate interplay between feature dimensionality and intrusion detection systems. We rely on the renowned IEEE 802.11 security-oriented AWID3 dataset to implement our experiments since AWID was the first dataset created from wireless network traffic and has been developed into AWID3 by capturing and studying traces of a wide variety of attacks sent into the IEEE 802.1X extensible authentication protocol (EAP) environment. This research unfolds in three distinct phases, each strategically designed to enhance the efficacy of our framework, using multi-nominal class, multi-numeric class, and binary class. The best accuracy achieved was 99% in the three phases, while the lowest accuracy was 89.1%, 60%, and 86.7% for the three phases consecutively. These results offer a comprehensive understanding of the intricate relationship between wireless dataset dimensionality and intrusion detection effectiveness.
Accurate segmentation of fruit based on deep learning Elsoud, Esraa Abu; Alidmat, Omar; Abuowaida, Suhaila; Alhenawi, Esraa; Alshdaifat, Nawaf; Aburomman, Ahmad; Chan, Huah Yong
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1331-1338

Abstract

In the last few years, deep learning has exhibited its efficacy and capacity in the field of computer vision owing to its exceptional precision and widespread acceptance. The primary objective of this study is to investigate an improved approach for segmentation in the context of various fruit categories. Despite the utilization of deep learning, the current segmentation techniques for various fruit items exhibit subpar performance. The proposed enhanced multiple fruit segmentation algorithm has the following main steps: 1) modifying the size of the filter, 2) the process of optimizing the ResNet-101 block involves selecting the most suitable count of repetitions. The multiple fruit dataset is split 80% in the training stage and 20% in the testing stage. These images were utilized to train a deep learning (DL) based algorithm, which aims to identify multiple fruit items within images accurately. The proposed algorithm has a lower training time compared to the other algorithms. The thresholds exhibit greater values compared to the thresholds of state-of-the-art algorithms.
Co-Authors Abuowaida, Suhaila Aburomman, Ahmad Al-Sit, Waleed Alidmat, Omar Alshdaifat, Nawaf Alshraiedeh, Fuad Chan, Huah Yong Elsoud, Esraa Elsoud, Esraa Abu Salah, Zaher